Face mask is an efficient tool to fight the Covid-19 pandemic and some factors increase the probability of its adoption

This study examines the dynamic impact of face mask use on both infected cases and fatalities at a global scale by using a rich set of panel data econometrics. An increase of 100% of the proportion of people declaring wearing a mask (multiply by two) over the studied period lead to a reduction of around 12 and 13.5% of the number of Covid-19 infected cases (per capita) after 7 and 14 days respectively. The delay of action varies from around 7 days to 28 days concerning infected cases but is more longer concerning fatalities. Our results hold when using the rigorous controlling approach. We also document the increasing adoption of mask use over time and the drivers of mask adoption. In addition, population density and pollution levels are significant determinants of heterogeneity regarding mask adoption across countries, while altruism, trust in government and demographics are not. However, individualism index is negatively correlated with mask adoption. Finally, strict government policies against Covid-19 have a strong significant effect on mask use.

from survey statistics to design and execute two components: (1) sampling design and (2) survey weights, which make the sample more representative of the general population". "We provide weights for two sets of sample respondents separately for both the CMU US and UMD global surveys. First, we provide weights for respondents who answered the questions needed to calculate the aggregate estimates of COVID-like Illness (CLI) reported in the CMU and UMD APIs. Second, we provide weights for a larger set of respondents who answered a minimum of two questions in the surveys." We extracted data from Maryland University link at https://covidmap.umd.edu/api.html. The surveys ask respondents how many people in their household are experiencing COVID-like symptoms, among other questions. These surveys are voluntary, and individual survey responses are held by University of Maryland and are shareable with other health researchers under a data use agreement. No individual survey responses are shared back to Facebook. Using this survey response data, we estimate the percentage of people in a given geographic region that use face mask cover. We use the smoothed weighted (two sets of sample respondents separately are used (CMU US and UMD global surveys)) percentage of survey respondents across an one week window that have reported use mask cover (see https://covidmap.umd.edu/document/css methods brief.pdf). More precisely, we used the following percent m c variable: weighted percentage of survey respondents that have reported use mask cover.
Using the survey data, the authors of the survey estimate the percentage of people in a given country or region, on a given day that use mask cover. The 2 -Require closing (only some levels or categories, eg just high school, or just public schools).

-Require closing all levels.
No data -blank.
1 -Recommend closing (or work from home).
2 -Require closing (or work from home) for some sectors or categories of workers.
3 -Require closing (or work from home) all but essential workplaces (eg grocery stores, doctors (January, 13, 2020 as the 100 basis) and are computed as the % change since the January, 13, 2020 basis.
Apple's mobility trend reports show how human mobility has changed in countries and cities worldwide since January 2020 and are based on location data of Apple's "maps" services. It is designed to help mitigate the spread of COVID-19, provide governments, research institutions, health authorities, and the general public with insights on the effects on human mobility of national and regional lockdown policies. The data covers 63 countries. All data are shared on an aggregated level and Apple does not keep a history of users' mobility behaviour. Data from Google reports have also been considered as an alternative. We proceed to some tests and the dynamics from both databases are very similar.
Since Apple data are available on a more longer period (January, 13, 2020 versus February, 15, 2020), we have chosen Apple data in our paper. We conducted a set of additional regressions and robustness checks using our panel data of 96 countries to test the effectiveness of mask use and then concerning the cross-sectional work about the mask use determinants.

Cross-sectional data for country-level
Age65: Population aged over 65 as a percentage of the total population from the World Bank Database.
Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
Density: Population density has been collected from the World Bank Database and is computed as the population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes. School: School enrollment data from the World Bank Database are used to proxy education impact on mask use. Primary school enrollment in % is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.
Last available year is 2017. The qualitative question asked respondents how willing they would be to give to good causes without expecting anything in return on an 11-point scale. The quantitative scenario depicted a situation in which the respondent unexpectedly received 1,000 euros and asked them to state how much of this amount they would donate (Table I).
T olerance: We used the tolerance intentions index from the World Value Survey database. More precisely, we selected answers to the question 12 untitled 'Tolerance and respect for other people' from the survey 2017-2020 untitled 'Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five'. Our index has been computed by reporting the percentage (over the total of responses) of 'important' responses.
Government conf idence: We also tested the possibility that trust in politicians and government can impact the compliance in line with Bargain and Aminojov (2020). To do that, we used data concerning the question 71 untitled 'Confidence: The government' from the survey 2017-2020 (World Value Survey database). We computed the percentage score concerning the "not at all" answer. In our opinion, it is a mean to capture the proportion of people in a given country that do not trust at all in the government and public guidance and thus could reject public guidance in favor of mask adoption and/or requirements.

Risk aversion:
We collected the risk aversion Degree of Ambiguity Aversion at the Country Level from Rieger et al. (2015). We assume that heterogeneous risk preferences could diverge the behaviors of using masks as well as COVID-19 severity.

Effectiveness of mask use: additional regressions and robustness checks
In addition to the robustness tests presented in tables 5-7 in the main manuscript, we also conducted other robustness checks concerning endogeneity and potential omitted issues and by controlling sample biases.

Taking into account additional control policies variables
The existence of a significant correlation between the number of mas wearing people and the number of infected cases and fatalities respectively is a controversial special issue. Some critiques have been addressed to the Zhang et al. (2020) work, especially the fact that the authors do not consider other non pharmaceutical mitigation policies in their statistical analysis as well as the length of the studied sample, notably in the New-York case.
Here, we consider additional control variables and especially include variables about other non pharmaceutical mitigation policies. Considering our time varying panel day by day database, we should incorporate some time varying variables to proxy other policy measures. We first consider the number of new tests per 100k inhabitants to investigate if the mask wearing effect is not driven by the existence of a simultaneous testing policy. We find that the effect of mask wearing is robust to the presence of a 'tests' variable for 14 days in the core of the paper (Table 4) but we extend this test to more lags for the infected cases (Table 9) and for fatalities (Table 10). As a consequence, the mask effect is not falsely significant due to the potential success of a simultaneous testing control policy.
We also add the stringency index (stringency) in Tables 11 and 12 to control the existence of other control/lockdown measures during the same periods the masks have been used. Thus, we are able to disentangle the effects from the masks and the effects stemming from other control and mitigation measures. The mask use effect is negative and significant at high significance level (P < 0.01) for lags 14 and 28 (infected cases) and lag 14 and 42 for fatality rates regressions (at a lower significance level).     *** p<0.01, ** p<0.05, * p<0.1   *** p<0.01, ** p<0.05, * p<0.1 *** p<0.01, ** p<0.05, * p<0.1

Taking into account additional controls: individual mobility and temperatures
We also control for mobility (driving index here). If individuals are more mobile, they are likely to use more masks to go outside (at work, at school or university, at the supermarket). Above all, the mobility can be viewed as a another proxy of the control policies (lockdown, stay-at-home requirements, restrictions on public gatherings etc): if the mobility is strongly reduced, it is an other indirect indicator that the level of stringency of the control policies is high.
We presented previous results with temperatures in the core of the manuscript (see Table 5), for 67 available countries. Considering our data about mobility and temperatures, we are working on a shorter sample of European countries here. When we add other control variables, the number of countries dropped to 22.
For transparency concerns, we first present the results of the mask wearing impact before introducing additional controls: temperatures, mobility and new tests per 100k inhabitants. The mask variable is negatively associated to the cases ratio with very high level of significance (P < 0.01) for 7 and 14 days/lags. Then, we add other controls in this benchmark regression.

Matrix of correlation and potential collinearity
We have checked the complete matrix of correlations to avocountries collinearity problems. For example, rich countries characterized by higher GDP levels (in natural logarithms units) have a high probability to have also a high proportion of elder people and high government effectiveness index. As a consequence, a mutlivariable regression with GDP , Age65 and gov ef f ect simultaneously is not robust due to the presence of collinearity issues.

Partial linear regressions
In our cross-sectional database, we select mask use proportion variable on July, 15, 2020 corresponding to the latest observation of our daily frequency panel dataset. We also select the mask use proportion variable in the beginning period (this date varies from April, 23 to April, 25, 2020 according to the countries) to take into account the dynamics of the pandemic and the potential adjustments in human behaviors regarding the mask wearing as well as the effects of the control policies implemented by the governments.
We have tested partial linear regressions for all potential determinants. We have considered the presence of potential quadratic forms even though our cross-section sample is of a limited size. In the following tables, we present the most suitable specifications and only report the most significant regression: for instance, population density regression does not incorporate significant quadratic variable whereas CO2 emissions influence on mask use is working via a quadratic form; in other words, only highly polluted countries are characterized by a positive influence of pollution level (level of CO2 emissions in natural logarithm) on the percentage of population wearing a face mask.
These partial regressions give information about the potential socioeconomic determinants but need to be cautiously analysed due to the presence of potential issues, especially omitted variable bias and serial correlation issues.
3.3. Partial linear regressions: observations on July, 15, 2020 (2) (3)    We have tested the effect of education using the last available data about the schooling variable from the World Bank and also the last PISA score. We expect that a better education level enhance the mask wearing as well as hygiene and compliance in a general way. We have tested the effect of overweight using the last available data about the overweight proportion variable.  Some studies assume that differences in government and politicians trust can impact the effectiveness of control policies (see for instance Bargain and Aminijonov, 2020). We computed the proportion of people that have 'not trust at all' in their government from World Value Survey database (2017-2020 survey) as a "trust in government" proxy. A high proportion of people that do not trust in government is not associated to a reduction of mask wearing proportion (see figure 9). However, note that the number of observation is limited and results need to be cautiously interpreted. We can not completely confirm that people that do not trust at all are the mask offenders and explain heterogeneity about mask wearing across countries.

Figure 1: Mask adoption versus trust in government index
Note: The X-axis refers to the mask adoption proportion: a 0.8 value means that 80 respondents over 100 declared to use a face mask. The Y-axis denotes the trust in government index (a large value indicates that a high proportion of people are not confident at all in their government. 'maskfinal' denotes the proportion of people using a face mask in the final observation of our sample (15th July, 2020). The red line refers to a simple linear fit.